BLP-2LASSO for aggregate discrete choice models with rich covariates

Summary We introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen (2013). Economists often study consumers’ aggregate behaviour across markets choosing from a menu of differentiated products. In this analysis, local demographic characteristics can serve as controls for market-specific preference heterogeneity. Given rich demographic data, implementing these models requires specifying which variables to include in the analysis, an ad hoc process typically guided primarily by a researcher’s intuition. We propose a data-driven approach to estimate these models, applying penalized estimation algorithms from the recent literature in high-dimensional econometrics. Our application explores the effect of campaign spending on vote shares in data from Mexican elections.

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A data-driven approach to conditional screening of high-dimensional variables

Stat ◽

10.1002/sta4.115 ◽

2016 ◽

Vol 5 (1) ◽

pp. 200-212 ◽

Cited By ~ 6

Author(s):

Hyokyoung G. Hong ◽

Lan Wang ◽

Xuming He

Keyword(s):

Data Driven ◽

High Dimensional ◽

Data Driven Approach

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Bundling in Molecular Dynamics Simulations to Improve Generalization Performance in High-Dimensional Neural Network Potentials

10.26434/chemrxiv.7860032 ◽

2019 ◽

Author(s):

Yasuharu Okamoto

Keyword(s):

Neural Network ◽

Force Field ◽

Data Driven ◽

High Dimensional ◽

Field Calculation ◽

Force Field Calculations ◽

Quantitative Accuracy ◽

Force Field Calculation ◽

Data Driven Approach ◽

Dynamics Simulations

High dimensional neural network potential (HDNNP) is interested as an alternative to classical force field calculations by data-driven approach. HDNNP has an advantage over classical force field calculation, such as being able to handle chemical reactions, but there are many points yet to be understood with respect to the chemical transferability in particular for non-organic compounds. In this paper, we focused on Au13+ and Au11+ clusters and showed that the energy of clusters of different sizes can be predicted by HDNNP with semi-quantitative accuracy.

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A Data-Driven Approach for Multiscale Elliptic PDEs with Random Coefficients Based on Intrinsic Dimension Reduction

Multiscale Modeling and Simulation ◽

10.1137/19m1277485 ◽

2020 ◽

Vol 18 (3) ◽

pp. 1242-1271

Author(s):

Sijing Li ◽

Zhiwen Zhang ◽

Hongkai Zhao

Keyword(s):

Dimension Reduction ◽

Random Coefficients ◽

Data Driven ◽

Elliptic Pdes ◽

Intrinsic Dimension ◽

Data Driven Approach

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ASigNTF: Agnostic Signature using NTF – a universal agnostic strategy to estimate cell-types abundance from transcriptomic datasets

10.1101/2021.02.04.429589 ◽

2021 ◽

Author(s):

Paul Fogel ◽

Galina Boldina ◽

Corinne Rocher ◽

Charles Bettembourg ◽

George Luta ◽

...

Keyword(s):

Biological Material ◽

Ad Hoc ◽

Immune Cell ◽

Cell Types ◽

Data Driven ◽

Molecular Signatures ◽

Tensor Factorization ◽

Rna Seq ◽

Data Driven Approach

AbstractBackgroundMolecular signatures for deconvolution of immune cell types have been proposed, based on a methodology that relies on the biological classification of the cell types being studied. When working with less known biological material, a data-driven approach is needed to uncover the underlying classes and construct ad hoc signatures.ResultsWe introduce a new approach, ASigNTF: Agnostic Signature using Non-negative Tensor Factorization, to perform the deconvolution of cell types from transcriptomics data (RNAseq and microarray). ASigNTF, which is based on two complementary statistical/mathematical tools: non-negative tensor factorization (for dimensionality reduction) and the Herfindahl-Hirschman index (for signature selection), can be applied to any type of tissue as long as transcriptomic data on isolated cells is available. As a direct result of the new method, we propose two new signatures for the deconvolution of immune cell types, one consisting of a relatively small set of 415 genes, which is more compatible with microarray platforms, and a larger set of 915 genes. Using external datasets, our two signatures outperform the CIBERSORT LM22 signature in deconvolution of RNA-seq data. Our signature with 415 genes allows to recognize a larger number of cell types compared to the ABIS microarray signature.ConclusionsThe paper proposes a new method, ASigNTF; applies the method, and also provides a software implementation that allows to identify molecular signatures for deconvolution of complex tissues and specifically up to 16 immune cell types from micro-array or RNA-seq data.HighlightsSeveral signatures of immune cell types have been proposed, which follow a methodology deeply rooted in the known biological classification of the investigated cell types.When working with less known biological material, a more agnostic, data-driven approach is required to uncover the underlying classes and construct ad hoc signatures.We present ASigNTF, a new agnostic approach to cell type classification and signature selection supported by an application software.We discuss the results of benchmarking our proposed signatures, ABIS-seq and CIBERSORT on external datasets.

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Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling

Sensors ◽

10.3390/s21051646 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1646

Author(s):

Alireza Entezami ◽

Hassan Sarmadi ◽

Behshid Behkamal ◽

Stefano Mariani

Keyword(s):

Multidimensional Scaling ◽

Damage Detection ◽

Health Monitoring ◽

Large Scale ◽

Environmental Variability ◽

Data Driven ◽

High Dimensional ◽

Data Driven Approach ◽

Low Dimensional ◽

High Dimensional Datasets

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.

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